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基于深度学习的磁共振成像在冠心病诊疗中的应用进展

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随着人口老龄化增长趋势的日益显现以及现代生活方式的改变,冠心病的患病率逐年递增且呈现年轻化趋势,成为当今全球最常见的致死性疾病之一.传统的影像技术己无法满足逐年递增以及日渐复杂的病例需求,医师的压力剧增.近年来,人工智能的快速发展有效提升了医师的工作效率和准确度,各种新兴人工智能技术与影像设备的结合在临床实践中取得的积极效果,展现出了光明的发展前景,尤其是能同时评估心脏结构和功能信息的无创心脏磁共振技术.本文就深度学习与磁共振成像技术结合在冠心病的诊疗应用过程中的研究现状、进展以及局限性做出综述,旨在提高医师诊疗的效率和准确性,促使冠心病能被及时诊断而得到早期干预治疗,同时推动人工智能在我国影像医学领域的发展进步.
Application of deep learning-based magnetic resonance imaging in the diagnosis and treatment of coronary artery disease
With the increasing trend of population aging and changes in modem lifestyle,the prevalence of coronary artery disease is increasing year by year and gradually showing a younger trend,making it one of the most common fatal diseases in the world today.Traditional imaging technology can no longer meet the demand of the increasing and increasingly complex cases year by year,and the pressure of physicians'is increasing dramatically.In recent years,the rapid development of artificial intelligence has effectively improved the efficiency and accuracy of physicians'work,and the combination of various emerging artificial intelligence technology and imaging equipment has achieved positive results in clinical practice,showing a bright future for development,especially the non-invasive cardiac magnetic resonance technology that can simultaneously evaluate the structure and function of heart.This paper summarizes the research status,progress and limitations of the combination of deep learning and magnetic resonance in the diagnosis and treatment of coronary artery disease,aiming to improve the efficiency and accuracy of physicians'diagnosis and treatment,promote the timely diagnosis and early intervention of coronary artery disease,and promote the development and progress of artificial intelligence in the field of imaging medicine in China.

coronary artery diseasecardiovascular diseasemagnetic resonance imagingdeep learningartificial intelligenceimaging diagnosis

伍倩、郭辉

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新疆医科大学第四临床医学院,乌鲁木齐 830099

冠状动脉性心脏病 心血管疾病 磁共振成像 深度学习 人工智能 影像诊断

2024

磁共振成像
中国医院协会 首都医科大学附属北京天坛医院

磁共振成像

CSTPCD北大核心
影响因子:1.38
ISSN:1674-8034
年,卷(期):2024.15(11)